The workforce challenge: How can technology ease problems of recruitment and workforce planning?

In the middle of the winter crisis of 2016, the Portuguese government introduces some changes to the labour legislation. One of these changes was a cut in nurses’ contracted hours, reducing them from 40 to 35 hours per week. This had a huge impact on hospitals’ productivity, particularly surgical departments, considering the limitations of NHS hospitals to recruit in a timely manner.

One of the many institutions affected by this change embraced the challenge as an opportunity to reassess its operational model and brought in Glintt for support. The goal was to identify a solution that would i) minimize the impact on the total number of surgeries performed throughout the year, ii) at a minimum extra cost, iii) while keeping the patients’ waiting times within the defined intervals for each pathology and priority level.

The first step was to identify bottlenecks and to define a comprehensive and assertive problem framing. An analytics-based diagnose collected and run historical data from ten different Information Systems concerning: HR planning and scheduling of all different staff, surgery demand per specialty, surgical production and teams, and income per surgery. All this data was analysed in light of information gathered from interviews with clinical directors, for context.

Technology enabled advanced analytics techniques to unveil patterns in demand per specialty, to estimate the duration of each individual surgery in the waiting list, to identify the required surgeons’ skills, and to predict the extra costs with staff (see details here).

This phase revealed that, given the master surgery scheduling in place, the bottleneck would actually depend on the specialty. For some specialties it was the nurses’ hours, while for others it was operation room capacity. In fact, the total number of nursing hours was balanced with the total number of hours needed. It was the skills matrix of the nurse team that required adjustment.

Crossing cluster techniques applied to the historical data of the nurse teams in each surgery with the information collected during interviews with the nursing director, enabled the identification of which teams should be modified to balance the required nursing hours per specialty.

In order to propose new scenarios for the master surgery scheduling with the 35 hours per week limitation, it was necessary to run simulations with different constraints and objective functions. For example, it was very important to simulate the staff’s extra hours payment the new plan would generate and the production needed. Besides the financial perspective on this topic, the hospital was also very conscious that whether it was dealt with fairness of not, it would have a huge impact on staff retention and motivation.

In the end, technology enabled the hospital to find the best available solution to the complex multi-skill, multi-period problem that is human resources planning and scheduling. The problem which started intuitively as a recruitment need was clarified and a new master surgery scheduling was implemented. Technology was key to achieve a balanced solution that

included operational, financial, compliance and human resources perspectives and ensured the best available trade-off between:

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